AWS Machine Learning Blog

Category: Artificial Intelligence

Use Amazon SageMaker Data Wrangler for data preparation and Studio Labs to learn and experiment with ML

Amazon SageMaker Studio Lab is a free machine learning (ML) development environment based on open-source JupyterLab for anyone to learn and experiment with ML using AWS ML compute resources. It’s based on the same architecture and user interface as Amazon SageMaker Studio, but with a subset of Studio capabilities. When you begin working on ML […]

Announcing Visual Conversation Builder for Amazon Lex

Amazon Lex is a service for building conversational interfaces using voice and text. Amazon Lex provides high-quality speech recognition and language understanding capabilities. With Amazon Lex, you can add sophisticated, natural language bots to new and existing applications. Amazon Lex reduces multi-platform development efforts, allowing you to easily publish your speech or text chatbots to […]

QuickSight Visualization

Get better insight from reviews using Amazon Comprehend

“85% of buyers trust online reviews as much as a personal recommendation” – Gartner Consumers are increasingly engaging with businesses through digital surfaces and multiple touchpoints. Statistics show that the majority of shoppers use reviews to determine what products to buy and which services to use. As per Spiegel Research Centre, the purchase likelihood for […]

Prepare data at scale in Amazon SageMaker Studio using serverless AWS Glue interactive sessions

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps, including preparing data and building, training, and deploying models. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and […]

How Medidata used Amazon SageMaker asynchronous inference to accelerate ML inference predictions up to 30 times faster

This post is co-written with Rajnish Jain, Priyanka Kulkarni and Daniel Johnson from Medidata. Medidata is leading the digital transformation of life sciences, creating hope for millions of patients. Medidata helps generate the evidence and insights to help pharmaceutical, biotech, medical devices, and diagnostics companies as well as academic researchers with accelerating value, minimizing risk, […]

Deploy large models on Amazon SageMaker using DJLServing and DeepSpeed model parallel inference

The last few years have seen rapid development in the field of natural language processing (NLP). Although hardware has improved, such as with the latest generation of accelerators from NVIDIA and Amazon, advanced machine learning (ML) practitioners still regularly encounter issues deploying their large language models. Today, we announce new capabilities in Amazon SageMaker that […]

Tips to improve your Amazon Rekognition Custom Labels model

In this post, we discuss best practices to improve the performance of your computer vision models using Amazon Rekognition Custom Labels. Rekognition Custom Labels is a fully managed service to build custom computer vision models for image classification and object detection use cases. Rekognition Custom Labels builds off of the pre-trained models in Amazon Rekognition, which […]

Use ADFS OIDC as the IdP for an Amazon SageMaker Ground Truth private workforce

To train a machine learning (ML) model, you need a large, high-quality, labeled dataset. Amazon SageMaker Ground Truth helps you build high-quality training datasets for your ML models. With Ground Truth, you can use workers from either Amazon Mechanical Turk, a vendor company of your choosing, or an internal, private workforce to enable you to […]

How Amp on Amazon used data to increase customer engagement, Part 2: Building a personalized show recommendation platform using Amazon SageMaker

Amp is a new live radio app from Amazon. With Amp, you can host your own radio show and play songs from the Amazon Music catalog, or tune in and listen to shows other Amp users are hosting. In an environment where content is plentiful and diverse, it’s important to tailor the user experience to […]

How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

Amp, the new live radio app from Amazon, is a reinvention of radio featuring human-curated live audio shows. It’s designed to provide a seamless customer experience to listeners and creators by debuting interactive live audio shows from your favorite artists, radio DJs, podcasters, and friends. However, as a new product in a new space for […]